Relational Learning with GPUs: Accelerating Rule Coverage

• Tipo: Artigo em Revista Científica Internacional
• Data: 2016

Autores

Alberto Martinez Angeles, CA (Autor) Outra; Wu, HC (Autor) Outra; Ines Dutra (Autor) FCUP; Costa, VS (Autor) FCUP; Buenabad Chavez, J (Autor) Outra

Revista

Título: International Journal of Parallel Programming

Vol. 44

Páginas: 663-685

ISSN: 0885-7458

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00K-9ZT

DOI: 10.1007/s10766-015-0364-7

Abstract (EN): Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 23